{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_excel('./体测分数_男生.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df2 = pd.read_excel('./体测分数_女生.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['dj'] = pd.cut(df1.男1000米跑,bins=3,labels=['优秀','中等','普通'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "普通    261\n",
      "中等    195\n",
      "优秀     21\n",
      "Name: dj, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(pd.value_counts(df1['dj']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['dj2'] = pd.cut(df1.男引体,bins=3,labels=['普通','中等','优秀'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "中等    230\n",
      "普通    227\n",
      "优秀     20\n",
      "Name: dj2, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(pd.value_counts(df1['dj2']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of      班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "476  17  男     0.00          0   0.00        0    0      0     0       0    0   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  dj dj2  \n",
       "0        0  2785      62  170  72.599998  25.120001  普通  普通  \n",
       "1       60  3133      68  174  52.700001  17.410000  普通  普通  \n",
       "2        0  3901      80  169  46.500000  16.280001  普通  普通  \n",
       "3        0  4946     100  183  79.699997  23.799999  普通  普通  \n",
       "4       68  3538      74  171  54.700001  18.709999  中等  中等  \n",
       "..     ...   ...     ...  ...        ...        ...  ..  ..  \n",
       "472      0  4647     100  176  69.500000  22.440001  普通  普通  \n",
       "473     50  7042     100  177  76.000000  24.260000  普通  普通  \n",
       "474     85  5755     100  181  65.000000  19.840000  中等  中等  \n",
       "475     76  5688     100  172  51.700001  17.480000  普通  中等  \n",
       "476      0     0       0    0   0.000000   0.000000  优秀  普通  \n",
       "\n",
       "[477 rows x 19 columns]>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '男1000米跑等级饼图')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JV03K5+iROdzxfgOxPXMpzhNOGZvLNYfl06tAmDoyh0cXNnTXj2HbIMyuVFoIX2jNfXQyZhK2F5c0cut/6jnsrio+WJ1g1qKGLV3iD1cn2KVX67/CvQZks7wsyaUH5m15bdGGJMN7C/k50mrg09TpxCMn2i6iM4TrOG08cgDwGhl6AsVhd1XxxGmFHHJnFZOH5fDMZ43MPa+ILyqS3Oc1cO0R/3314S9frmVEnyzO3NOEtrxOeWtlggMGZ3PInVX84tB8vrtHro0fxZb1wJiwH78NT2jjkULgI8xF0BltU43ywpJGJg3NZkBxGDtLVs0kXnac7SK2R5h+41fiAgtA7x7CyWNyXWC3zbHEI2fZLmJ7hKOljUdGAPMwI4GOs702AiOJl220Xci2CMtH9Z9wgXU6Tx/gl7aL2FbBb2njkWOAmbbLcNJOIxAlXvax7UI6KtgtbTxSQJqeP+pYl0NIpyMKdmjhcmCY7SKctPUt4pFv2C6io4LbPY5HIpgbMEVsl+KktQXAOOJlLZ/EHUBBbmkvwgXW6XqjgTNsF9ERwWxp45ESYClmlM9xutoCYCzxsgCG4euC2tJeiAus031GA8fYLqK9ghfaeKQIuNR2GU7G+antAtoreKGF/8XdKMvpfgcSj4TinsXBCm08koXpGjuODVfYLqA9ghVaOAoYarsIJ2MdTTwy2nYRbQlaaL9vuwAn451ju4C2BOeQTzyyI2aS6Yy6KtsJnNXA4CCfbBGklvYcXGAd+wYAgT61MRihjUcE+J7tMhzHF7NdwNYEI7RwCG5WCic4jiUe6WW7iNYEJbRpMUuekzbygdNsF9Ea+6E1XePjbZfhOM2cZLuA1lgP7d09S/Zam52d1/aSjtOtDvYvDw0c66G9vm/vYycPGdjvwKGD5/98h76vzM/L+8x2TY6DOZLxTdtFtCQIk34fjYhUioyZVVLErJIiclSX711bt3RaeUXJodU10Zxg1OlkiKTKuk910KJ7EkeOvxYetF1Pc1ZProiWRncA1rCVFl9UNw9raFxwYkWlHFdROaZENSPu4eN0H1US5RQumJ3ca8OMxsk7vq277w4iwOql06fuZLu+5myHdhrmZlrto9rQN5n0jq6sqpxWXjF8cGNiUNdV56SzhMraT3TIoocTh2Y/ljhkjzKKe7Wy6Lil06d63VlbW2x3Ow/v0NIiuRuys8ffG+nJvZGeFCSTiw6uqf1yWllF/33q6nYXyIg7JTsdp0rjZooWvJQcv/Hexik7va8jdgPp3463Hg640KbYe3veXJuVtduLRYW7vVhUSJbql2Pr6j89rbyi8Kiq6miem9w84yU068sFOnTxQ4lDcx5PHLRHBUXjtmE1gbshtbXucbQ0mgtUAp1/uEe1cnBj47zjKqoSJ1dU7tE7mXRT12QAVRo2UjLvxcQ+5TMSkwd+pMNHdsJqP1s6fWpnrKfT2AztnsAHXb4h1UTPZHL+kVU1m84sLx86vKFxly7fptNtGjXrC0+HLX4wcXj+rMSBo6voUdIFm+mzdPrUTe1dWEQOAD5W1c0icjgwB8hR1eo23pelqsm21m+ze7xdXeN2E8kuz84e92jPYh7tWUyu6uf71dQun1Ze0eegmtoxWQE4Vu20nyr1G+g579nEhIoZiSmDF+rQ4Zg7vXel/YDnOrD8nzGnQW4GfoW5auhpETlLVZeLyDjgMKA30B8ziWExUCcip6lqw9ZWnv6hbaZBZNicwh7D5hT2QFTX71bf8PHJFZW5366sGluoWmSjJmfrGjR7xYc6/PMHEocXPJ3Yf0w1BeO7uYQJtDO0IrIfsE5Vm04SSgI1mMkKc0XMoSTgdcxNrr8BNKjqXf77s0VEdCtdYJvd41cxV/cEg2pt/0TC+05lVe1p5ZW77ZhI7Gi7pEylSu1aes1/NrFf5YzE5J0X6RDbt4Z5dOn0qe26qEVEnsSEsR9mMHQf4F3/21lAHLgKqAd2AQYCBcCHwAbMGM+Fqrqo1W3YCG20NCpAGdAV+x/bT1WLVBceWl2z7oyyioHR+vpADUSko3rNWfq+jlh+f+MRhc8mJ4ypJb+H7ZpSzFs6fWq0rYVE5FjgAmAVcJ6qJkTkHeBJYDbwGpBUVRWRPYAbgKeAHsAU4BpVfaPN7VgK7Qjg027f8DbKVl25V23dktPLK0oOr64Zm+tm2NhuqlSvps/8pxL719yXmDx0iQ4M8oR+tUDR0ulTtzpIJCLfwtx/6nJVPVtE8oE3gSeA2ao6W0RGAtcAY4FfAOOBbOA/wFlAL+B8VW01H7b2acdY2u42SYgMfrdHweB3exSAatkuDY3zT6yo5PjKyjE9kxrIK0GCqE5zlryTHLXi/sQRJc8n9x1TT+6E7VlfomoTWQUlSHaX/xkXAIOB5VtbSFWfFpHBKS8dCbzfbLHFwK8x4TwHc5/cl4AbVXWYiAwHlmxtO7ZGTsO7vygSWZqXO/EPfXtPPGjnwYWTdh70/m/79H51eU7OStulBY0qlSt1h7dvb/z2q4fW3bByVN3du05ruPLQJ5MHjq8nNx9M8FbdedFW15O6TPm7s1h9709I1tdS8/n73RHYJrtsw3suBmY1PRGRgcDOqrpQVd/ENJp/w4QWEXkT+PbWBqHAXkvbz9J2O5dI7qbs7L3vj5Rwf6SE/GTy04k1tavOKK/oN6G2bo9MPK2yVnM/ezu5+xczEpN7vpQcP6aBnP22tvyml/+JNtZvdZ2pyzSs/ZyisYdTv3oRktutJ70NA15tx3ICICI/AXbCdI1HYA7xnAKsBH4jIqcCxar6QdP6ReR8oK6tDbjQdqK6rKyRLxcVjnzZnFa5ZnR9/aJTyisLjq6qiuYrBbbr6wqqlK/QfgufSE6sfyBxxPCV2m8E5o+0TTXLPkRy88ku6tXuZVQVTTRS8/n7RCae0gk/QbsNbOdyuZju9BhgmqomReQOzA2+FgNPiMgbwFzg7KY3icjvMN3pNkepXWi7SFJkx3n5+TvO65fP1Tv0qRrYmHjruMrKhpPLK3fvm0yG+l5F1Zr3ydzk6NUzEpMjryT3HNNITofPz9VEA2Vz7qf/d69i7WPXtnuZHsP2ptJ7kcIR+7Pu0V8TOfAUCoZuyynFHdauU2FVdQlwarPXNgN3Nz0XkcNVtXmLerWqtusmYLZCG+o/2g4TKVqVm7P/bb17cVuvSLIkqd7k6uqNZ5RVDBnV0LCr7fLaokrZUt1xwczkQYkHGw8f8SV9RwGjtmedZXMfoWT8t8kqKO7QMkV7TCInsiONm7+kx/AJVC+a012h7d1ZK2ohsKjq1vcRUriWtruJZFVkS/TxkmIeLzGnVe5rTqvsfXBN7ZhsM/xvlSpaTf7Hc5Jj18xITO77WnLc6CRZB3bmNmqXfkDtsg+peO9J6td+zoZn/kTfoy9q1zING78gt88gkrVVdOMhy8BcdOJCa1mDyLA3C3sMe9OcVrlxREPDwpPLK7O/U1k1tki19WaokyWVTZ/rTgsfSxySfDhx6Ki19N4D2KOrtjdg2u+2fL36visomXAcm169h96Tzmx1mb5HX0Syrprsot7k9h3ChuduodfEbpvpNDChtXVyRS3uetetU63rl0h4Uyura04vrxixUyLRqdOeqJKsouDjV5Pj1s5ITNnhjeTo0UqWu3iidR8tnT51T9tFgIXQRkujRZjraJ0O6JFMfjypumbNmeUVA/asq9+m/cmkyvrPdOCiRxOT9JHEpFEbiGTW2ML2Wbx0+tR2jYp3NdszVzjtVJOVtftzxUW7P1dcRLbqF+Pq6hafXl5ZfERV9di8ViYSUCVZQeH82ck9N85onNzvbd19dyVrYnfXniasjzU0sRHaNg8eO1uXEBn0fkHBoPcLCkC1fOfGxndPqKhMnlBRNaY4oQ2LdMgnjyQmZT+aOGT3zZS0eaK70y6BCa2tfdoE7uLzzqfaqEi57TLSU9aqeWd/GIgPQFvd43pIzzOErBLJkQCNcqaXZJntCprYau3afSDZcQJiq1PAdCdboXX7tU7YNNouoIlraR2nfVxLa2m7jrOt1tkuoIkLreO0T2AmObAV2o2Wtus42+oL2wU0sRXaFZa26zjbKuNb2q1OkOU4AeRCa2m7jrOtMr577ELrhE3Gt7ShmajccYA6L+Zl/CGfxQToDBPHacMq2wWkshJaL+Y10MYs6o4TIAttF5DK5uVxn1jctuN0xFzbBaSyGVrP4rYdpyPesl1AKpuhbc8tFhzHNsWFdos5uMEoJ/g+9mJeYC6AB4uh9WJeJfCere07TjsFan8W7M/T9Irl7TtOW1xom3GhdYLOhbaZ14Gk5RocpzWVwHzbRTRnNbT+Dv4HNmtwnK14xYt5CdtFNGe7pQXXRXaC62HbBbQkCKF9wXYBjtOCemCm7SJaEpTQrrVdhOM084IX8zbbLqIl1kPrxbxG4AHbdThOM4HsGkMAQuu7x3YBjpOiHnjcdhGtCURovZj3DvCx7Tocx/dC0E5dTBWI0Ppca+sExUO2C9iaIIV2BuaKCsexKbCjxk0CE1ov5i3DXa7n2PdYkLvGEKDQ+lwX2bHtRtsFtCVooX0Qd8sQx565Xsx723YRbQlUaP1rbG+yXYeTsW62XUB7BCq0vj8Bgd6ncNLSMuAR20W0R+BC6w8C/Ml2HU7G+b1/dl7gBS60vhuBCttFOBljNfBP20W0VyBD68W8TcCttutwMsYfvZhXa7uI9gpkaH03AFW2i3DS3jrgdttFdERgQ+vf8ChU/5lOKF3hH7UIjcCG1nc9Zp4ex+kKbwF32i6iowIdWi/mrQGusV2Hk5aSwIVezAvd+e6BDq3vJmCe7SKctHOHf0lo6AQ+tP6xswts1+GklY3Az20Xsa0CH1oAL+a9CpTarsNJG1d6MW+D7SK2VShC6/sR5iC40w6JmgSJ6sBN2RsE7wF/s13E9hDV8OyHR0ujxwOP2a6jM2lCWXT5InL75QIw8IyBFAwpIFmf5NMrP2XU9aNafF/5u+Wsf369WUeDkl2YTZ8j+rB25lp2vmhnaj6roWSfErJywvS53OUSwEFezAvUrSs7KlShBYiWRh8CTrJdR2epWVpD2dtlDDh5wH+9vuaRNax/bj1j/j6mzXWse3odef3zqFpQRfG4YrRe0Ual18ReXVR1aP3ci3nX2S5ie4XxY/gC4EvbRXSW6sXVlL9bzpLfLGHF7SvQhFK3qo7albX02LVHm+9P1iepnF9JZN8ICGi9UvVJFYWjCruh+lB5Fphuu4jOELrQ+mdKnQg02K6lM/QY1oNhPxvGrlfuSnZhNhUfVbD6wdXsNG2ndr1/8xub6XVALwAi+0XY8OIG8vrnsfqB1Wx+c3PXFR4uK4Ezw3hMtiWhCy2AF/PeAC62XUdnKBhSQG4vsz+bv1M+tctqKRxVSF6/vHa9v2xuGZH9IgAUjSpi8PmDye2TS16/PCoXuJPJgEbgVC/mrbddSGcJZWgBvJj3F0J4ClpzK/+2kprlNWhSKX+vnI2zN1LxQQVLrltC7fJalt24rNX31q+rJ6tHFln5X/0aqz+ppnBkIZItiEh3/AhBd5UX8+bYLqIz5dguYDv9LzAWmGC7kG3V/9j+rLh9BQA99+rJsJ8O2/K9JdctYeiPhtJY2ciah9Yw6NxB//XeyvmVFI0q2vJck4rkCTmRHKo/q6Zk75Lu+SGC62ng97aL6GyhGz1uLloaHQK8C/SzXYsTKCuAvcN8EkVrQts9buLFvBXAyZh9F8cB2Ax8Jx0DC2kQWgAv5s0GLrNchhMM1cBUL+Z9aLuQrpIWoQXwYt7NwK9s1+FYVQ981z+6kLbSJrQAXsz7JfAb23U4ViSAaV7Me852IV0trUIL4MW8q4Df2a7D6XbnezEvFPMWb6+0Cy2AF/OuAP5ouw6n2/zYi3n/sF1Ed0nL0AJ4Me8y3C1GMsG1Xsy7wXYR3Sn0x2nbEi2N/hm40HYdTqdTzEyKaXfyRFvSPrQA0dLo9bhDQumkATjXi3n32i7EhowILUC0NBoD/grk267F2S4VwAlezHvBdiG2ZExoAaKl0QMwM1+077o3J2iWYc508mwXYlPaDkS1xIt5czEXF4Ry6swMNweYkOmBhQwLLYAX874ADgFm2K7Fabe7gCP8CRAyXkZ1j5uLlkZ/AlxHBn54hUQZcJEX8+62XUiQZHRoAaKl0aMw9yYd1NayTrd6CTjHi3nLbRcSNBnfwngx73nMhfQZefgggGqBS4ApLrAty/iWNlW0NHoc5rBQf8ulZKp3gLO8mLfQdiFBlvEtbSov5j0O7IHpLrtPs+7TiLms8kAX2La5lrYV0dLoJEyru7vtWtLcK5gT/t+1XUhYuNBuRbQ0mgdcCvwE6G25nHTjYc4dftp2IWHjQtsO0dJoT8wNwH4ERCyXE3YrgF8Ad3sxL2m7mDByoe2AaGm0N/Bj4CIg4+cn7aBNmGPif/ZiXq3tYsLMhXYbREujfYHLMZf8FbWxeKYrx4wNXOfFvE22i0kHLrTbIVoa7YcJ73lAH8vlBM0HwF+A+7yY5+5P0olcaDtBtDSaDxwDnAMcBWTbrciaWuAh4C/+xRlOF3Ch7WTR0ugg4CxMgEdaLqe7fAbcDtyVrhOEB4kLbReKlkYPAs7F3AQ73QauVgAzgX8BL6fLbSTDwIW2G/jHew8AJvuP/Qnfzc+SmNMMnwFmejHvfcv1ZCwXWguipdFiYBJfhXgcELT7Uiqm2/sS8ALwkhv9DQYX2gDwR6EnAVFgjP8YSfe1xuswZyilPuZ7Ma+qm7bvdIALbUBFS6O5wK4pj2H+oxfm2HBhyr9NX6eOWicwd4/b1MpjDTAf8LyYt6arfx6n87jQphF/37kIE9gKNziUnlxoHSdk3PW0jhMyLrSdTIxdUp6PFJGvnSElIrkiErQRYycEXGg7kR/CQuCfIlIoIjnAPfjT1/iBvl9EegBXAM+KyPMi8qyI1IjI9+1V74SF26ftRCIyE8jDXDxQhPlQrAMqgRpVPUpELgFqVfV2EekJzAI+Bj5U1dvsVO6EiQttJxKRAZi5jh4DBgAFmECeA/xWVT8RkSzMiQvXAcWY6WwGAy8CfVX1NBu1O+ERtlPpAktERmJOiMjGnLJYAuRipqnpBQwXkVOBI4DvAaOAfwBfACOAZ4GLu71wJ3RcaDtP06DSwy18r+k83Uf85Qowt2ssBnpg7uRX3NUFOunBhbaTqOoiEakB/gPMa/bt0cAkVf1MRE7CdI8F0+L2xrTKA4B+3ViyE1IutJ2rAXN64IvNXu+Nmds3VY6qThGRw4B9MTeZuq+L63PSgAtt50oCq/l6aA8C8I/X7g0MBd7zv7ccc9rhgcCdIpKvqnXdU64TRi60nSsf2BOY3uz10Zj/6//DzJ30OnCqiDzvvycXs0/bF3PD6xu6qV4nhNwhn27kn2yRVFU336+zzVxoHSdk3GmMjhMyLrSOEzIutI4TMi60jhMyLrSOEzIutI4TMi60jhMyLrSOEzIutI4TMi60jhMyLrSOEzIutI4TMi60jhMyLrSOEzL/Dz7cIRVG4R/wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "labels = ['优秀','中等','普通']\n",
    "pre = [21,195,261]\n",
    "plt.pie(x = pre, # 数据\n",
    " labels=labels,\n",
    " autopct='%0.1f%%'# 显示标签\n",
    ") \n",
    "plt.title('男1000米跑等级饼图')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '男引体跑等级饼图')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "labels = ['优秀','中等','普通']\n",
    "pre = [20,227,230]\n",
    "plt.pie(x = pre, # 数据\n",
    " labels=labels,\n",
    " autopct='%0.1f%%'# 显示标签\n",
    ") \n",
    "plt.title('男引体跑等级饼图')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['djnp'] = pd.cut(df2.女800米跑分数,bins=4,labels=['不及格','普通','中等','优秀'])\n",
    "df2['djnt'] = pd.cut(df2.女跳远分数,bins=4,labels=['不及格','普通','中等','优秀'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "优秀     384\n",
      "中等     147\n",
      "不及格     37\n",
      "普通      25\n",
      "Name: djnp, dtype: int64\n",
      "中等     413\n",
      "优秀     148\n",
      "不及格     19\n",
      "普通      13\n",
      "Name: djnt, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(pd.value_counts(df2['djnp']))\n",
    "print(pd.value_counts(df2['djnt']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '女800跑等级')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pre = [384,147,37,25]\n",
    "labels = ['优秀','中等','普通','不及格']\n",
    "plt.bar(labels,pre\n",
    ") \n",
    "plt.title('女800跑等级')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '女跳远等级')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pre = [148,413,13,19]\n",
    "labels = ['优秀','中等','普通','不及格']\n",
    "plt.bar(labels,pre\n",
    ") \n",
    "plt.title('女跳远等级')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    549\n",
      "True      44\n",
      "Name: BMI, dtype: int64\n",
      "False    519\n",
      "True      74\n",
      "Name: BMI, dtype: int64\n",
      "False    578\n",
      "True      15\n",
      "Name: BMI, dtype: int64\n",
      "True     438\n",
      "False    155\n",
      "Name: BMI, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#女生BMI各情况人数\n",
    "print(pd.value_counts(df2['BMI']>=25.3))\n",
    "print(pd.value_counts((df2['BMI']>=22.8) & (df2['BMI']<=25.2) ))\n",
    "print(pd.value_counts(df2['BMI']<=16.4))\n",
    "print(pd.value_counts((df2['BMI']>=16.5) & (df2['BMI']<=22.7) ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    439\n",
      "True      38\n",
      "Name: BMI, dtype: int64\n",
      "False    500\n",
      "True      93\n",
      "Name: BMI, dtype: int64\n",
      "False    453\n",
      "True      24\n",
      "Name: BMI, dtype: int64\n",
      "True     364\n",
      "False    229\n",
      "Name: BMI, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#男生BMI各情况人数\n",
    "print(pd.value_counts(df1['BMI']>=26.4))\n",
    "print(pd.value_counts((df1['BMI']>=23.3) & (df2['BMI']<=26.3) ))\n",
    "print(pd.value_counts(df1['BMI']<=16.4))\n",
    "print(pd.value_counts((df1['BMI']>=16.5) & (df2['BMI']<=23.2) ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1f7a1e220>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1=[364/477,24/477,93/477,38/477]\n",
    "p2=[438/539,15/539,74/539,44/539]\n",
    "labels = ['正常','低体重','超重','肥胖']\n",
    "plt.figure(figsize=(9,9))\n",
    "plt.pie(p1,radius=1,autopct='%0.2f%%',wedgeprops=dict(linewidth=3,width=0.4,edgecolor='w'),labels = labels)\n",
    "plt.pie(p2,\n",
    " autopct='%0.2f%%',\n",
    " radius=0.7,\n",
    " pctdistance=0.7,\n",
    " wedgeprops=dict(linewidth=3,width=0.7,edgecolor='w'),labels = labels)\n",
    "plt.legend(labels,loc = 'upper right',bbox_to_anchor = (0.75,0,0.4,1),title =\n",
    "'BMI占比情况')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for /: 'list' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-146-d4f3b3f07982>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mp1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m364\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m24\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m93\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m38\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mp3\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m470\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'list' and 'int'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of      班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95   41   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72   36   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76   35   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68   41   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68   46   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI   dj djnp djnt  \n",
       "0       85  3775     100  163.0  51.299999  19.309999   普通   优秀   优秀  \n",
       "1       66  3683     100  163.0  66.599998  25.070000   普通   普通   中等  \n",
       "2       76  3331     100  157.0  60.000000  24.340000   普通   优秀   中等  \n",
       "3       85  3701     100  160.0  50.700001  19.799999   普通   优秀   优秀  \n",
       "4       70  3592     100  167.0  63.900002  22.910000   中等   优秀   普通  \n",
       "..     ...   ...     ...    ...        ...        ...  ...  ...  ...  \n",
       "588     78  2255      70  158.0  49.000000  19.629999  NaN   优秀   中等  \n",
       "589     72  2937      85  161.0  55.700001  21.490000  NaN   优秀   中等  \n",
       "590     72  2592      76  165.0  48.599998  17.850000  NaN   优秀   中等  \n",
       "591     78  1829      60  154.0  43.599998  18.379999  NaN   中等   中等  \n",
       "592     85  2962      85  162.0  55.299999  21.070000  NaN   普通   优秀  \n",
       "\n",
       "[593 rows x 20 columns]>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
